Hospitalization scenarios

Scenario definitions

• Tree canopy

  • Population-based Scenario: AI: Increase by 10% in all zip codes

  • Targeted

    • Scenario AII1: Increase by 10% in zip codes in the lowest 1/5th of current TC cover (i.e. <=20th pctile)

    • Scenario AII2: Increase by 10% in zip codes in the highest 1/5th of the Social Vulnerability Index (i.e. >80th pctile)

    • Scenario AII3: Increase by 10% in zip codes in the highest 1/5th of hospitalization burden (i.e. >80th pctile)

  • Proportionate-universalism

    • Scenario AIII1: Increase by 10% for bottom 1/5th of current TC cover… down to 2% for top 1/5th

    • Scenario AIII2: Increase by 10% for top 1/5th of SVI … down to 2% for bottom 1/5th

    • Scenario AIII3: Increase by 10% for top 1/5th of hospitalization burden … down to 2% for bottom 1/5th

• Impervious surface cover

  • Population-based: Scenario BI: Decrease by 10% in all zip codes

  • Targeted

    • Scenario BII1: Decrease by 10% in zip codes in the highest 1/5th of current imperv cover (i.e. >80th pctile)

    • Scenario BII2: Decrease by 10% in zip codes in the highest 1/5th of the Social Vulnerability Index (i.e. >80th pctile)

    • Scenario BII3: Decrease by 10% in zip codes in the highest 1/5th of hospitalization burden (i.e. >80th pctile)

  • Proportionate-universalism

    • Scenario BIII1: Decrease by 10% for top 1/5th of current imperv cover … down to 2% for bottom 1/5th

    • Scenario BIII2: Decrease by 10% for top 1/5th of SVI … down to 2% for bottom 1/5th

    • Scenario BIII3: Decrease by 10% for top 1/5th of hospitalization burden … down to 2% for bottom 1/5th

Explore distribution of IRR, IRD, and perc. diff

Facet by type of intervention (impervious surfaces vs tree canopy) and by scenario type - Population-based, Proportionate Universalism, Targeted

facet_histogram_fun=function(df){
  df %>% 
      ggplot(aes(value))+
      geom_histogram()+
      facet_grid(
        rows=vars(scenario_type_7_abbrev),
        cols=vars(scenario_intervention)
      )
}
hosp_all_long %>% 
  filter(measure=="irr") %>%
  facet_histogram_fun()+
  xlab("IRR")

#All IRD
hosp_all_long %>% 
  filter(measure=="ird") %>%
  facet_histogram_fun()+
  xlab("IRD")

#Exclude outliers
hosp_all_long %>% 
  filter(measure=="ird") %>%
  filter(value<0.005) %>% 
  facet_histogram_fun()+
  xlab("IRD")

hosp_all_long %>% 
  filter(measure=="pd") %>%
  facet_histogram_fun()+
  xlab("PD")

Static maps

Ideas for static maps of measures using facet plots

Population-based scenarios: Impervious surfaces vs. Tree (IRR)

Proportionate Universalism and Targeted Scenarios (IRR)

Impervious Surfaces

Tree

Interactive maps (IRR)

Population-based: Imp vs Tree (side by side)

Population-based: Imp vs Tree (separate layers)

Please navigate to the layer icon under the Zoom icon and select the layer corresponding to each type of intervention.